The Nature Index 2026 Research Leaders reveal the leading institutions and countries/territories in the natural sciences, health sciences, applied sciences and social sciences, according to their ...

Biological tissues are made up of different cell types arranged in specific patterns, which are essential to their proper functioning. Understanding these spatial arrangements is important when ...

A new software tool, ovrlpy, improves quality control in spatial transcriptomics, a key technology in biomedical research. Developed by the Berlin Institute of Health at Charité (BIH) in international ...

Single-cell RNA transcriptomics allows researchers to broadly profile the gene expression of individual cells in a particular tissue. This technique has allowed researchers to identify new subsets of ...

Biological tissues are made up of different cell types arranged in specific patterns, which are essential to their proper functioning. Understanding these spatial arrangements is important when ...

Single-cell RNA sequencing has transformed biology by showing which genes are active in individual cells. However, this approach requires cells to be removed from their natural environment, erasing ...

Biological systems are inherently three-dimensional—tissues form intricate layers, networks, and architectures where cells interact in ways that extend far beyond a flat plane. To capture the true ...

Spatial transcriptomics is transforming how scientists see biology—literally—by mapping gene activity in its original location inside tissues. From decoding tumor architecture to charting entire ...

Fei Chen and Chenlei Hu at the Broad Institute of MIT and Harvard have developed a new imaging-free spatial transcriptomics technology that tracks the diffusion of DNA barcodes between beads in an ...

Spatial biology is a rapidly advancing discipline that examines biological molecules (such as DNA, RNA, and proteins) within their native locations in tissues. This approach offers critical insight ...

This figure shows how the STAIG framework can successfully identify spatial domains by integrating image processing and contrastive learning to analyze spatial transcriptomics data effectively.